import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
		import jax

		from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel




class   _UpperCamelCase (		unittest.TestCase					):


			"""simple docstring"""






			def __init__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__=13 ,  lowerCAmelCase__=30 ,  lowerCAmelCase__=2 ,  lowerCAmelCase__=3 ,  lowerCAmelCase__=True ,  lowerCAmelCase__=True ,  lowerCAmelCase__=32 ,  lowerCAmelCase__=5 ,  lowerCAmelCase__=4 ,  lowerCAmelCase__=37 ,  lowerCAmelCase__="gelu" ,  lowerCAmelCase__=0.1 ,  lowerCAmelCase__=0.1 ,  lowerCAmelCase__=10 ,  lowerCAmelCase__=0.02 ,  )	->   Union[str, Any]:

							'''simple docstring'''


							__lowercase					     =				parent
							__lowercase					     =				batch_size
							__lowercase					     =				image_size
							__lowercase					     =				patch_size
							__lowercase					     =				num_channels
							__lowercase					     =				is_training
							__lowercase					     =				use_labels
							__lowercase					     =				hidden_size
							__lowercase					     =				num_hidden_layers
							__lowercase					     =				num_attention_heads
							__lowercase					     =				intermediate_size
							__lowercase					     =				hidden_act
							__lowercase					     =				hidden_dropout_prob
							__lowercase					     =				attention_probs_dropout_prob
							__lowercase					     =				type_sequence_label_size
							__lowercase					     =				initializer_range

							# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
							__lowercase					     =				(image_size // patch_size) ** 2
							__lowercase					     =				num_patches + 1





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Dict:

							'''simple docstring'''


							__lowercase					     =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

							__lowercase					     =				ViTConfig(
							    image_size=self.image_size ,  patch_size=self.patch_size ,  num_channels=self.num_channels ,  hidden_size=self.hidden_size ,  num_hidden_layers=self.num_hidden_layers ,  num_attention_heads=self.num_attention_heads ,  intermediate_size=self.intermediate_size ,  hidden_act=self.hidden_act ,  hidden_dropout_prob=self.hidden_dropout_prob ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob ,  is_decoder=lowerCAmelCase__ ,  initializer_range=self.initializer_range ,  )

							return config, pixel_values





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Optional[int]:

							'''simple docstring'''


							__lowercase					     =				FlaxViTModel(config=lowerCAmelCase__		)
							__lowercase					     =				model(lowerCAmelCase__		)
							# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
							__lowercase					     =				(self.image_size, self.image_size)
							__lowercase					     =				(self.patch_size, self.patch_size)
							__lowercase					     =				(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
							self.parent.assertEqual(result.last_hidden_state.shape ,  (self.batch_size, num_patches + 1, self.hidden_size)		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   List[str]:

							'''simple docstring'''


							__lowercase					     =				self.type_sequence_label_size
							__lowercase					     =				FlaxViTForImageClassification(config=lowerCAmelCase__		)
							__lowercase					     =				model(lowerCAmelCase__		)
							self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.type_sequence_label_size)		)

							# test greyscale images
							__lowercase					     =				1
							__lowercase					     =				FlaxViTForImageClassification(lowerCAmelCase__		)

							__lowercase					     =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
							__lowercase					     =				model(lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   str:

							'''simple docstring'''


							__lowercase					     =				self.prepare_config_and_inputs()
							(
							    (
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	
							)					     =				config_and_inputs
							__lowercase					     =				{'''pixel_values''': pixel_values}
							return config, inputs_dict




@require_flax
class   _UpperCamelCase (		_UpperCAmelCase		,unittest.TestCase					):


			"""simple docstring"""






			__a    :					Any     					=		(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   None:

							'''simple docstring'''


							__lowercase					     =				FlaxViTModelTester(self		)
							__lowercase					     =				ConfigTester(self ,  config_class=lowerCAmelCase__ ,  has_text_modality=lowerCAmelCase__ ,  hidden_size=37		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Optional[int]:

							'''simple docstring'''


							self.config_tester.run_common_tests()





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Any:

							'''simple docstring'''


							__lowercase					     =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Optional[int]:

							'''simple docstring'''


							__lowercase					     =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[str]:

							'''simple docstring'''


							__lowercase     ,	__lowercase					     =				self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											__lowercase					     =				model_class(lowerCAmelCase__		)
											__lowercase					     =				inspect.signature(model.__call__		)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											__lowercase					     =				[*signature.parameters.keys()]

											__lowercase					     =				['''pixel_values''']
											self.assertListEqual(arg_names[:1] ,  lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Tuple:

							'''simple docstring'''


							__lowercase     ,	__lowercase					     =				self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__		):
															__lowercase					     =				self._prepare_for_class(lowerCAmelCase__ ,  lowerCAmelCase__		)
															__lowercase					     =				model_class(lowerCAmelCase__		)

															@jax.jit
															def model_jitted(lowerCAmelCase__ ,  **lowerCAmelCase__		):
																			return model(pixel_values=lowerCAmelCase__ ,  **lowerCAmelCase__		)

															with self.subTest('''JIT Enabled'''		):
																			__lowercase					     =				model_jitted(**lowerCAmelCase__		).to_tuple()

															with self.subTest('''JIT Disabled'''		):
																			with jax.disable_jit():
																							__lowercase					     =				model_jitted(**lowerCAmelCase__		).to_tuple()

															self.assertEqual(len(lowerCAmelCase__		) ,  len(lowerCAmelCase__		)		)
															for jitted_output, output in zip(lowerCAmelCase__ ,  lowerCAmelCase__		):
																			self.assertEqual(jitted_output.shape ,  output.shape		)





			@slow
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Optional[Any]:

							'''simple docstring'''


							for model_class_name in self.all_model_classes:
											__lowercase					     =				model_class_name.from_pretrained('''google/vit-base-patch16-224'''		)
											__lowercase					     =				model(np.ones((1, 3, 2_24, 2_24)		)		)
											self.assertIsNotNone(lowerCAmelCase__		)
import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
		print("""Googling.....""")
		__a    :	Tuple         =    """https://www.google.com/search?q=""" + """ """.join(sys.argv[1:])
		__a    :	List[str]         =    requests.get(url, headers={"""UserAgent""": UserAgent().random})
		# res.raise_for_status()
		with open("""project1a.html""", """wb""") as out_file:  # only for knowing the class
				for data in res.iter_content(1_0_0_0_0):
						out_file.write(data)
		__a    :	Dict         =    BeautifulSoup(res.text, """html.parser""")
		__a    :	Any         =    list(soup.select(""".eZt8xd"""))[:5]

		print(len(links))
		for link in links:
				if link.text == "Maps":
						webbrowser.open(link.get("""href"""))
				else:
						webbrowser.open(F'''https://google.com{link.get("href")}''')